shelfgap AGENTS.md

shelfgap AGENTS.md is an instructions file for Codex, OpenCode from keskinonur/shelfgap. It costs 1,736 tokens per session, scanned A, original, MIT.

A set of AGENTS.md instructions for the ShelfGap repository. ShelfGap is a buyer-decision simulator for small online merchants that compares products, offers, and buyer requirements using evidence from public listings.

In plain words
What is it for?
Use it when changing ShelfGap to follow its data model, preserve source evidence, distinguish exact products from alternatives, and handle unknown buyer requirements correctly.
Why use it?
It defines product rules that help keep the repository's comparisons evidence-based and prevent unsupported claims about sales, market share, or prices.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions Codex.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/keskinonur/shelfgap/agents-md
Clone the repo
git clone --depth 1 https://github.com/keskinonur/shelfgap

Made for: Codex, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for shelfgap AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/keskinonur/shelfgap/agents-md.svg)](https://agentmods.dev/instructions/keskinonur/shelfgap/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/keskinonur/shelfgap/agents-md"><img src="https://agentmods.dev/badge/instructions/keskinonur/shelfgap/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,736 This file is loaded in full into every session.
When invoked 1,736 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.01736 $0.01736
Opus 5 $0.00868 $0.00868
Sonnet 5 $0.00347 $0.00347
Haiku 4.5 $0.00174 $0.00174

Measured 5d ago against content hash 7a04dffdf2f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

shelfgap AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 212 lines

How it starts

The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ShelfGap repository instructions

Mission

Build ShelfGap, an evidence-backed buyer decision simulator for small e-commerce merchants.

A merchant supplies normalized offer facts and buyer profiles or selects the built-in fictional sample. ShelfGap compares the merchant product and offer against exact-product resellers and alternative products. It evaluates them from several buyer perspectives, explains why the merchant wins or loses, and recommends the smallest feasible merchandising actions that could improve selectability.

The product must never claim guaranteed sales uplift, market share, or the globally lowest price.

Non-negotiable product principles

  • Separate Product, Offer, and BuyerContext.
  • Treat public listing data as time-sensitive evidence, not permanent truth.
  • Mark every extracted value as observed, inferred, or unknown.
  • Preserve source URL, evidence snippet, observation timestamp, and confidence.
  • Distinguish exact-product offers from alternative products.
  • Apply hard buyer requirements before preference scoring.
  • Treat an unknown mandatory attribute as uncertain, not false.
  • Calculate effective purchase cost from observable price, shipping, fees, and immediately usable discounts.
  • Apply membership benefits only when the buyer profile has that active membership.
  • Explain every score using visible components.
  • Generate recommendations only from computed choice gaps and cited evidence.
  • Require merchant approval before any simulated or real change.
  • Keep fictional brands and neutral marketplace labels in the built-in demo.
  • A more specific category recommendation must remain truthful and taxonomically valid.

Scope discipline

P0:

  • Built-in fictional demo with no API key and no network dependency.
  • Real user-supplied offer and buyer-profile analysis with no network dependency.
  • Four buyer profiles plus a custom profile editor.
  • Exact-offer and alternative-product comparisons.
  • Deterministic eligibility, scoring, win-rate, gap analysis, and action cards.
  • Before/after simulation for approved actions.
  • Unit, integration, and one happy-path browser test.
  • Clear README and demo instructions.

Read the full file on GitHub · 212 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 212 lines · 1,736 tokens per session scan A 7a04dffdf2f9

Subscribe to this mod's changes

shelfgap AGENTS.md is an instructions file published in the GitHub repository keskinonur/shelfgap (0 stars, last pushed 1mo ago), licensed MIT. It adds 1,736 tokens to every session, about $0.0087 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.